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IFRS 9 and CECL Credit Risk Modelling and Validation - 1st Edition - ISBN: 9780128149409, 9780128149416

IFRS 9 and CECL Credit Risk Modelling and Validation

1st Edition

A Practical Guide with Examples Worked in R and SAS

Author: Tiziano Bellini
eBook ISBN: 9780128149416
Paperback ISBN: 9780128149409
Imprint: Academic Press
Published Date: 15th January 2019
Page Count: 316
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IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.

Key Features

  • Offers a broad survey that explains which models work best for mortgage, small business, cards, commercial real estate, commercial loans and other credit products
  • Concentrates on specific aspects of the modelling process by focusing on lifetime estimates
  • Provides an hands-on approach to enable readers to perform model development, validation and audit of credit risk models


Upper-division undergraduates, graduate students, and professionals working in economic modelling and statistics.

Table of Contents

1. Introduction to Expected Credit Loss Modelling and Validation
1.1 Introduction
1.2 IFRS 9
1.21 Staging Allocation
1.22 ECL Ingredients
1.23 Scenario Analysis and ECL
1.3 CECL
1.31 Loss-Rate Methods
1.32 Vintage Methods
1.33 Discounted Cash Flow Methods
1.34 Probability of Default Method (PD, LGD, EAD)
1.35 IFRS 9 vs CECL
1.4 ECL and Capital Requirements
1.41 Internal Rating-Based Credit Risk-Weighted Assets
1.42 How ECL Affects Regulatory Capital and Ratios
1.5 Book Structure at a Glance
1.6 Summary

2. One-Year PDs
2.1 Introduction
2.2 Default Definition and Data Preparation
2.21 Default Definition
2.22 Data Preparation
2.3 Generalized Linear Models (GLMs)
2.31 GLM (Scorecard) Development
2.32 GLM Calibration
2.33 GLM Validation
2.4 Machine Learning (ML) Modelling
2.41 Classification and Regression Trees (CART)
2.42 Bagging, Random Forest, and Boosting
2.43 ML Model Calibration
2.44 ML Model Validation
2.5 Low Default Portfolio, Market-Based, and Scarce Data Modelling
2.51 Low Default Portfolio Modelling
2.52 Market Based Modelling
2.53 Scarce Data Modelling
2.54 Hints on Low Default Portfolio, Market-Based, and Scarce Data Model Validation
2.6 SAS Laboratory
2.7 Summary
2.8 Appendix A From Linear Regression to GLMs
2.9 Appendix B Discriminatory Power Assessment

3. Lifetime PDs 1
3.1 Introduction
3.2 Data Preparation
3.21 Default Flag Creation
3.22 Account-Level (Panel) Database Structure
3.3 Lifetime GLM Framework
3.31 Portfolio-level GLM Analysis
3.32 Account-Level GLM Analysis
3.33 Lifetime GLM Validation
3.4 Survival Modelling
3.41 Kaplan Meier (KM) Survival Analysis
3.42 Cox Proportional Hazard (CPH) Survival Analysis
3.43 Accelerated Failure Time (AFT) Survival Analysis
3.44 Survival Model Validation
3.5 Lifetime Machine Learning (ML) Modelling
3.51 Bagging, Random Forest, and Boosting Lifetime PD
3.52 Random Survival Forest Lifetime PD
3.53 Lifetime ML Validation
3.6 Transition Matrix Modelling
3.61 Na_ve Markov Chain Modelling
3.62 Merton-Like Transition Modelling
3.63 Multi State Modelling
3.64 Transition Matrix Model Validation
3.7 SAS Laboratory
3.8 Summary

4. LGD Modelling
4.1 Introduction
4.2 LGD Data Preparation
4.21 LGD Data Conceptual Characteristics
4.22 LGD Database Elements
4.3 LGD Micro-Structure Approach
4.31 Probability of Cure
4.32 Severity
4.33 Defaulted Asset LGD
4.34 Forward-Looking Micro-Structure LGD Modelling
4.35 Micro-Structure Real Estate LGD Modelling
4.36 Micro-Structure LGD Validation
4.4 LGD Regression Methods
441 Tobit Regression
4.42 Beta Regression
4.43 Mixture Models and forward-looking Regression
4.44 Regression LGD Validation
4.5 LGD Machine Learning (ML) Modelling
4.51 Regression Tree LGD
4.52 Bagging, Random Forest, and Boosting LGD
4.53 Forward-Looking Machine Learning LGD
4.54 Machine Learning LGD Validation
4.6 Hints on LGD Survival Analysis
4.7 Scarce Data and Low Default Portfolio LGD Modelling
4.71 Expert Judgement LGD Process
4.72 Low Default Portfolio LGD
4.73 Hints on How to Validate Scarce Data and Low Default Portfolio LGDs
4.8 SAS Laboratory
4.9 Summary

5. Prepayments, Competing Risks and EAD Modelling
5.1 Introduction
5.2 Data Preparation
5.21 How to Organize Data
5.3 Full Prepayment Modelling
5.31 Full Prepayment via GLMs
5.32 Machine Learning (ML) Full Prepayment Modelling
5.33 Hints on Survival Analysis
5.34 Full Prepayment Model Validation
5.4 Competing Risk Modelling
5.41 Multinomial Regression Competing Risks Modelling
5.42 Full Evaluation Procedure
5.43 Competing Risk Model Validation
5.5 EAD Modelling
5.51 A Competing-Risk-Like EAD Framework
5.52 Hints on EAD Estimation via Machine Learning (ML)
5.53 EAD Model Validation
5.6 SAS Laboratory
5.7 Summary

6. Scenario Analysis and Expected Credit Losses
6.1 Introduction
6.2 Scenario Analysis
6.21 Vector Auto-Regression (VAR) and Vector Error-Correction (VEC) Modelling
6.22 VAR and VEC Forecast
6.23 Hints on GVAR Modelling
6.3 ECL Computation in Practice
6.31 Scenario Design and Satellite Models
6.32 Lifetime ECL
6.33 IFRS 9 Staging Allocation
6.4 ECL Validation
6.41 Historical and Forward-Looking Validation
6.42 Credit Portfolio Modelling and ECL Estimation
6.5 SAS Laboratory
6.6 Summary


No. of pages:
© Academic Press 2019
15th January 2019
Academic Press
eBook ISBN:
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About the Author

Tiziano Bellini

Tiziano Bellini received his PhD degree in statistics from the University of Milan after being a visiting PhD student at the London School of Economics and Political Science. He is Qualified Chartered Accountant and Registered Auditor. He gained wide risk management experience across Europe, in London, and in New York. He is currently Director at BlackRock Financial Market Advisory (FMA) in London. Previously he worked at Barclays Investment Bank, EY Financial Advisory Services in London, HSBCs headquarters, Prometeia in Bologna, and other leading Italian companies. He is a guest lecturer at Imperial College in London, and at the London School of Economics and Political Science. Formerly, he served as a lecturer at the University of Bologna and the University of Parma. Tiziano is author of Stress Testing and Risk Integration in Banks, A Statistical Framework and Practical Software Guide (in Matlab and R) edited by Academic Press. He has published in the European Journal of Operational Research, Computational Statistics and Data Analysis, and other top-reviewed journals. He has given numerous training courses, seminars, and conference presentations on statistics, risk management, and quantitative methods in Europe, Asia, and Africa.

Affiliations and Expertise

BlackRock Financial Market Advisory, London, UK


"IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples Worked in R and SAS by Tiziano Bellini is a precious resource for industry practitioners, researchers and students in the field of credit risk modeling and validation.  The author does a great job in covering the various topics in a scientifically sound and comprehensive way without losing practitioner focus. The SAS and R case studies further contribute to its value and make it indispensable for anyone working in credit risk!" --Bart Baesens, KU Leuven and the University of Southampton

"It is commendable that practitioners like Dr Tiziano Bellini find the time to write volumes on the important industry developments in risk management. This timely volume provides a guide to credit risk modelling and validation in the context of IFRS 9 and CECL expected credit loss estimates. The book is thus developed in the context of the familiar PD, LGD and EAD framework. Recent challenging developments are discussed, for example the treatment of lifetime losses is very timely. The last part of the book, where multivariate time series models are brought into play, can also give ideas to researchers who may wish to make their work more relevant for the industry. More generally, this volume provides an unparalleled guide for graduate and MSc students. Examples in R and SAS make the book a must-have for risk management practitioners." --Damiano Brigo, Imperial College London

Ratings and Reviews